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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Supervised categorical principal component analysis for genome-wide association analyses.

Meng Lu, Hye-Seung Lee, David Hadley

|February 26, 2014

Summary

This study introduces Supervised Categorical Principal Component Analysis (SCPCA) for analyzing genetic data in complex diseases. SCPCA improves upon existing methods by explicitly modeling categorical Single-Nucleotide Polymorphisms (SNPs) without assuming risk models, enhancing disease association detection.

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Last Updated: May 2, 2026

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Published on: July 27, 2021

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Genetics
Biostatistics
Computational Biology

Background:

Genome-Wide Association Studies (GWAS) often face challenges in explaining complex disease heritability.
Aggregated association analyses using multiple Single-Nucleotide Polymorphisms (SNPs) increase detection power for variants with weak individual effects.
Existing Principal Component Analysis (PCA) based methods for aggregated analysis may impose restrictive assumptions on genotype data or risk models.

Purpose of the Study:

To develop a novel aggregated association analysis method for complex diseases.
To address limitations of current PCA-based methods by relaxing assumptions on genotype data and risk effect models.
To introduce Supervised Categorical Principal Component Analysis (SCPCA) for enhanced SNP analysis.

Main Methods:

Developed a general Supervised Categorical Principal Component Analysis (SCPCA) method.
SCPCA explicitly models categorical SNP data without requiring risk effect model assumptions.
Evaluated SCPCA against Supervised PCA (SPCA) and Supervised Logistic PCA (SLPCA) using simulated and real genotype data.

Main Results:

SCPCA demonstrated superior performance compared to SPCA and SLPCA.
The superiority of SCPCA is attributed to its explicit modeling of categorical SNP data.
SCPCA's flexibility regarding risk effect models contributes to its enhanced efficacy.

Conclusions:

SCPCA offers a more robust approach for aggregated association analysis in complex diseases.
The method improves the detection of disease-associated SNPs, particularly those with subtle individual effects.
SCPCA provides a valuable tool for understanding unexplained heritability in genetic studies.